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“Improved Prediction of Gene Expression Through Integrating Cell Signalling Models with Machine Learning.” BMC Bioinformatics 23 (1): 323.
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“Mechanistic Models Versus Machine Learning, a Fight Worth Fighting for the Biological Community?” Biology Letters 14 (5): 20170660.
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Compagni, Riccardo Delli, Zhao Cheng, Stefania Russo, and Thomas P. Van Boeckel. 2022.
“A Hybrid Neural Network-SEIR Model for Forecasting Intensive Care Occupancy in Switzerland During COVID-19 Epidemics.” PLOS ONE 17 (3): e0263789.
https://doi.org/10.1371/journal.pone.0263789.
Gaw, Nathan, Andrea Hawkins-Daarud, Leland S. Hu, Hyunsoo Yoon, Lujia Wang, Yanzhe Xu, Pamela R. Jackson, et al. 2019.
“Integration of Machine Learning and Mechanistic Models Accurately Predicts Variation in Cell Density of Glioblastoma Using Multiparametric MRI.” Scientific Reports 9 (1): 10063.
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Jia, Xiaowei, Jared Willard, Anuj Karpatne, Jordan S. Read, Jacob A. Zwart, Michael Steinbach, and Vipin Kumar. 2021.
“Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles.” ACM/IMS Transactions on Data Science 2 (3): 1–26.
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Jorner, Kjell, Tore Brinck, Per-Ola Norrby, and David Buttar. 2021.
“Machine Learning Meets Mechanistic Modelling for Accurate Prediction of Experimental Activation Energies.” Chemical Science 12 (3): 1163–75.
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Pearl, Judea. 2019.
“The Seven Tools of Causal Inference, with Reflections on Machine Learning.” Communications of the ACM 62 (3): 54–60.
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von Rueden, Laura, Sebastian Mayer, Katharina Beckh, Bogdan Georgiev, Sven Giesselbach, Raoul Heese, Birgit Kirsch, et al. 2023.
“Informed Machine Learning A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems.” IEEE Transactions on Knowledge and Data Engineering 35 (1): 614–33.
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Willard, Jared, Xiaowei Jia, Shaoming Xu, Michael Steinbach, and Vipin Kumar. 2022.
“Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems.” ACM Computing Surveys, March, 3514228.
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Zampieri, Guido, Supreeta Vijayakumar, Elisabeth Yaneske, and Claudio Angione. 2019.
“Machine and Deep Learning Meet Genome-Scale Metabolic Modeling.” PLOS Computational Biology 15 (7): e1007084.
https://doi.org/10.1371/journal.pcbi.1007084.
Al taweraqi, Nada, and Ross D. King. 2022.
“Improved Prediction of Gene Expression Through Integrating Cell Signalling Models with Machine Learning.” BMC Bioinformatics 23 (1): 323.
https://doi.org/10.1186/s12859-022-04787-8.
Baker, Ruth E., Jose-Maria Peña, Jayaratnam Jayamohan, and Antoine Jérusalem. 2018.
“Mechanistic Models Versus Machine Learning, a Fight Worth Fighting for the Biological Community?” Biology Letters 14 (5): 20170660.
https://doi.org/10.1098/rsbl.2017.0660.
Compagni, Riccardo Delli, Zhao Cheng, Stefania Russo, and Thomas P. Van Boeckel. 2022.
“A Hybrid Neural Network-SEIR Model for Forecasting Intensive Care Occupancy in Switzerland During COVID-19 Epidemics.” PLOS ONE 17 (3): e0263789.
https://doi.org/10.1371/journal.pone.0263789.
Gaw, Nathan, Andrea Hawkins-Daarud, Leland S. Hu, Hyunsoo Yoon, Lujia Wang, Yanzhe Xu, Pamela R. Jackson, et al. 2019.
“Integration of Machine Learning and Mechanistic Models Accurately Predicts Variation in Cell Density of Glioblastoma Using Multiparametric MRI.” Scientific Reports 9 (1): 10063.
https://doi.org/10.1038/s41598-019-46296-4.
Jia, Xiaowei, Jared Willard, Anuj Karpatne, Jordan S. Read, Jacob A. Zwart, Michael Steinbach, and Vipin Kumar. 2021.
“Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles.” ACM/IMS Transactions on Data Science 2 (3): 1–26.
https://doi.org/10.1145/3447814.
Jorner, Kjell, Tore Brinck, Per-Ola Norrby, and David Buttar. 2021.
“Machine Learning Meets Mechanistic Modelling for Accurate Prediction of Experimental Activation Energies.” Chemical Science 12 (3): 1163–75.
https://doi.org/10.1039/D0SC04896H.
Pearl, Judea. 2019.
“The Seven Tools of Causal Inference, with Reflections on Machine Learning.” Communications of the ACM 62 (3): 54–60.
https://doi.org/10.1145/3241036.
von Rueden, Laura, Sebastian Mayer, Katharina Beckh, Bogdan Georgiev, Sven Giesselbach, Raoul Heese, Birgit Kirsch, et al. 2023.
“Informed Machine Learning A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems.” IEEE Transactions on Knowledge and Data Engineering 35 (1): 614–33.
https://doi.org/10.1109/TKDE.2021.3079836.
Willard, Jared, Xiaowei Jia, Shaoming Xu, Michael Steinbach, and Vipin Kumar. 2022.
“Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems.” ACM Computing Surveys, March, 3514228.
https://doi.org/10.1145/3514228.
Zampieri, Guido, Supreeta Vijayakumar, Elisabeth Yaneske, and Claudio Angione. 2019.
“Machine and Deep Learning Meet Genome-Scale Metabolic Modeling.” PLOS Computational Biology 15 (7): e1007084.
https://doi.org/10.1371/journal.pcbi.1007084.